Note: Descriptions are shown in the official language in which they were submitted.
SYSTEM AND METHOD OF SMART HEALTH MONITORING
BACKGROUND OF THE INVENTION
Field of the Art
[001] The present invention relates to a field of smart health monitoring.
More particularly, the
present invention relates to a system and method of smart health monitoring
for aquatic animals,
which implements Internet of Things technologies and machine leaning
techniques, to infer vital
health signals more accurately.
Discussion of the State of the Art
[002] Health and wellness are of utmost importance for any living being, be it
human or animal,
and so monitoring it on regular basis is a necessity. With the advancement in
technology, the
health monitoring systems have coordinately evolved over the years, with a
sole aim of assisting
people to actively monitor their health status with respect to fitness level
as well as medical level.
The measurement of vital signs is critical to the treatment and management of
many medical
conditions for a variety of target individuals or populations. Some of the
prior arts have been
described hereunder which disclose about the health monitoring devices,
systems and methods.
[003] The prior art W02021046237A1 discloses a wearable biometric sensor
technology for
physiological monitoring for medical, health, and fitness applications.
Further it discloses a
biometric waveform analysis system utilized to generate a physiological
assessment using a
wearable device having a sensor system, metric output generator, waveform
analysis, and control
processor configured to control the sensor system, the metric output
generator, and the waveform
analysis engine.
[004] Another prior art U510973422B2 discloses a device and method for non-
invasively
measuring arterial stiffness using pulse wave analysis of photoplethysmogram
data. The
wearable biometric monitoring devices for measuring arterial stiffness have
the ability to
automatically and intelligently obtain PPG data under suitable conditions
while the user is
engaged in activities or exercises, providing good-quality PPG data for PWA
while conserving
power of the wearable biometric monitoring devices.
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[005] Yet another prior art US20210110927A1 discloses a method of extracting a
respiratory
rate from a photoplethysmogram (PPG) using a machine learning model, wherein
an artificial
neural network model can be trained to predict the respiratory rate using a
training dataset
containing PPG data. The artificial neural network model can include a series
of convolutional
layers used to identify a PPG signal in the PPG data and remove artifacts
contained in the PPG
data, a fast Fourier transform (FFT) layer used to identify PPG frequencies in
the PPG data, and
a dense layer used to decode a respiratory rate value from the PPG
frequencies.
[006] Still another prior art U510433726B2 discloses health monitoring
systems, methods, and
devices to remotely monitor and track patient health status. The health-
monitoring system
comprising IoT-vitals sensing nodes joined to a patient's body, sensing vital
characteristics,
employing wireless transmission circuitry transmitting sensed data by a short-
range network, a
local gateway having wireless circuitry receiving transmitted data from the
IoT-vitals sensors,
software (SW) executing on a processor from a non-transitory medium, the SW
processing the
transmitted data received, and transmission circuitry transmitting processed
data over a long-
range network.
[007] Further prior art U520210022660A1 discloses systems and methods for
collecting and
analyzing vital sign information to predict a likelihood of a subject having a
disease or disorder,
wherein the system for monitoring a subject comprise sensors which are
configured to acquire
health data comprising vital sign measurements of the subject over a period of
time, and a mobile
electronic device to process the health data and provide output.
[008] Yet further prior art AU2019101396A4 is disclosed which describes about
a smart
personal monitoring system to be worn by a first user, comprising an
accelerometer to
continuously detect physical movements comprising falls of the first user, and
a
photoplethysmography (PPG) sensor to continuously measure biometric data.
Also, a live
messaging module is provided to send notification message to one or more
second users
associated with the first user through a network when event associated with
the first user occurs.
[009] Still further prior art U510722749B2 is disclosed which describes about
a predictive
health monitoring using neural networks, comprising a wearable device with
biometric sensors, a
database containing data from multiple users across many categories of health-
related factors, a
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first set of neural networks trained on the database that makes health
predictions based on a
single health factor, and a second neural network that makes health
predictions based on a
combination of the predictions made by the first set of neural networks.
[0010] Although devices, systems and methods for health monitoring have been
described by the
abovementioned prior arts, they are largely limited to only human health and
wellness. Therefore,
there remains a significant need and a void in the market to provide a smart
health monitoring
system and method for aquatic or marine animals. Because, by keeping aquatic
animals healthy,
the livelihoods of millions around the world can be secured, the diversity of
life below water can
be protected, and food security can be ensured for our future generations.
[0011] Aquatic animals can also be affected by infectious diseases just like
terrestrial animals
and humans, which may be caused by viruses, bacteria, fungi, protozoa and
parasites. Hence,
there exists a need to provide a smart wearable health monitoring system and
method to
effectively monitor health of aquatic animal to protect the sustainability of
commercial and
recreational fisheries, and the productivity of aquaculture industries.
[0012] Moreover, the conventional smart health monitoring systems do not teach
about
monitoring the vital signs by reconstructing an accurate 4-dimensional
representation of the
micro blood vessels in the targeted area of the human body, nor are they
specifically tailored
towards generating a personalized baseline profile. However, the smart health
monitoring system
of the present invention is applicable to human being with a customized
hardware design.
Therefore, the unique characteristics and features of the present invention
are unrepresented
within the conventional arts.
[0013] The applicant is unaware of the existence of any such system and method
to monitor
health of the aquatic animals that contains the abovementioned features and
addresses the above
described shortcomings in the prior arts. More specifically, there is no such
IoT-based system
and method known in the prior art that is capable of accurately and frequently
measuring vital
signs of the aquatic animals, wherein the vital signs include body
temperature, blood pressure,
peripheral capillary oxygen saturation (Sp02), pulse rate, and respiration
rate. Furthermore, no
such system exists which uses a data visualization platform and automated
machine learning
platform, to quickly analyze the health-related data from the aquatic animals
and generate a
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personalized baseline profile. Also, no existing system provides an extraction
of more detailed
vital signs from the 4D micro blood vessel time-series data captured via
various sensors to
reconstruct aquatic animal's hemodynamics system.
SUMMARY OF THE INVENTION
[0014] Accordingly, the inventor has conceived and reduced to practice, in a
preferred
embodiment of the invention, a smart health monitoring system and method which
is capable of
accurately as well as frequently measuring a plurality of vital health signs
including, but not
limited to, body temperature, blood pressure, peripheral capillary oxygen
saturation (Sp02),
pulse rate, and respiration rate.
[0015] An objective of the present invention is to provide a system and method
of smart health
monitoring which measures the vital health signs of the aquatic animals, in a
precise manner.
[0016] Another objective of the present invention is to provide a system and
method of smart
health monitoring which employs Internet of things technologies.
[0017] Still another objective of the present invention is to provide a system
and method of
smart health monitoring which utilizes machine learning models to predict the
vital health signs.
[0018] Still another objective of the present inventions is to provide a
system and method of
smart health monitoring which utilizes a data visualization platform to
analyze the health-related
data and generates a personalized baseline profile.
[0019] Also, one more objective of the present invention is to provide a
system and method of
smart health monitoring which reconstructs the aquatic animal's hemodynamics
system.
[0020] Another objective of the present invention is to provide a system and
method of smart
health monitoring which secures livelihoods of millions around the world,
protects the diversity
of life below water, and ensures the food security for the future generations.
[0021] Still another objective of the present invention is to provide a system
and method of
smart health monitoring which protects the sustainability of commercial and
recreational
fisheries, and the productivity of aquaculture industries.
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[0022] The present invention therefore introduces a novel IoT-based system and
method of smart
health monitoring which comprises an attachable device having a
microcontroller and circuitry,
and an external processing device, for effectively predicting the vital health
signs of the aquatic
animals. The attachable device employs an imaging device and a plurality of
sensors to collect
the plurality of vital health signs of the aquatic animal. The attachable
device is attached on a
targeted area of the aquatic animal under measurement. The attachable device
can be customized
in design to be applicable on any aquatic animal depending on size and shape
of the aquatic
animal.
[0023] Additionally, the smart health monitoring system and method of the
present invention
utilizes the machine learning models to generate a medical model of the
individual aquatic
animal to obtain more accurate vital health signals. Furthermore, the smart
health monitoring
system also tracks fluctuations in the vital health signal data.
BRIEF DESCRIPTION OF THE DRAWINGS
[0024] The present invention is illustrated and described herein with
reference to the various
drawings, in which like reference numbers denote the various components and/or
steps, as
appropriate. The drawings illustrate example embodiments of the present
disclosure and cannot
be considered as limiting its scope.
[0025] FIG. 1 is a block diagram of a smart health monitoring system,
according to an
embodiment of the present invention.
[0026] FIG. 2 is a block diagram of an attachable device of a smart health
monitoring system,
according to an embodiment of the present invention.
[0027] FIG. 3 is a block diagram of a power module of an attachable device,
according to an
embodiment of the present invention.
[0028] FIG. 4 is a flow diagram that illustrates a method of obtaining a
plurality of vital health
signals more accurately by implementing a smart health monitoring system,
according to an
embodiment of the present invention.
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[0029] FIG. 5 is a flow diagram that illustrates an example method for
obtaining a vital health
signal prediction from a machine learning model implemented in a smart health
monitoring
system, according to an embodiment of the present invention.
[0030] The figures are described in greater detail in the next section of the
patent specification.
DETAILED DESCRIPTION OF THE INVENTION
[0031] The foregoing disclosure has broadly outlined the features and
technical advantages of
the present disclosure in order that the description of the disclosure that
follows may be better
understood. It should be appreciated by those skilled in the art that the
conception and specific
embodiment disclosed may be readily utilized as a basis for modifying or
designing other
structures for carrying out the same purposes of the present disclosure. The
novel features which
are believed to be characteristic of the disclosure, both as to its
organization and method of
operation, together with further objects and advantages will be better
understood from the
following description. The specification concludes with claims defining the
features of the
systems and methods that are regarded as novel, it is believed that the
systems and methods will
be better understood from a consideration of the following description in
conjunction with the
drawing figures, in which like reference numerals are carried forward.
[0032] Before the systems and methods are disclosed and described, it is to be
understood that
the terminology used herein is for the purpose of describing particular
embodiments only and is
not intended to be limiting. The terms "comprises," "comprising," or any other
variation thereof
are intended to cover a non-exclusive inclusion, such that a process, method,
article, or apparatus
that comprises a list of elements does not include only those elements but may
include other
elements not expressly listed or inherent to such process, method, article, or
apparatus. An
element proceeded by "comprises . . . a" does not, without more constraints,
preclude the
existence of additional identical elements in the process, method, article, or
apparatus that
comprises the element. The terms "including" and/or "having," as used herein,
are defined as
comprising (i.e., open language). The terms "a" or "an", as used herein, are
defined as one or
more than one. The term "plurality," as used herein, is defined as two or more
than two. The
term "another," as used herein, is defined as at least a second or more. The
description may use
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the terms "embodiment" or "embodiments," which may each refer to one or more
of the same or
different embodiments.
[0033] For the purposes of the description, a phrase in the form "A/B" or in
the form "A and/or
B" or in the form "at least one of A and B" means (A), (B), or (A and B),
where A and B are
variables indicating a particular object or attribute. When used, this phrase
is intended to and is
hereby defined as a choice of A or B or both A and B, which is similar to the
phrase "and/or".
Where more than two variables are present in such a phrase, this phrase is
hereby defined as
including only one of the variables, any one of the variables, any combination
of any of the
variables, and all of the variables, for example, a phrase in the form "at
least one of A, B, and C"
means (A), (B), (C), (A and B), (A and C), (B and C), or (A, B and C).
[0034] Herein various embodiments of the systems and methods are described. In
many of the
different embodiments, features are similar. Therefore, to avoid redundancy,
repetitive
description of these similar features may not be made in some circumstances.
It shall be
understood, however, that description of a first-appearing feature applies to
the later described
similar feature and each respective description, therefore, is to be
incorporated therein without
such repetition.
[0035] Headings of sections provided in this patent application and the title
of this patent
application are for convenience only, and are not to be taken as limiting the
disclosure in any
way.
[0036] Devices that are in communication with each other need not be in
continuous
communication with each other, unless expressly specified otherwise. In
addition, devices that
are in communication with each other may communicate directly or indirectly
through one or
more communication means or intermediaries, logical or physical.
[0037] A description of an embodiment with several components in communication
with each
other does not imply that all such components are required. To the contrary, a
variety of optional
components may be described to illustrate a wide variety of possible
embodiments of the
inventions and in order to more fully illustrate one or more aspects of the
inventions. Similarly,
although process steps, method steps, algorithms or the like may be described
in a sequential
order, such processes, methods and algorithms may generally be configured to
work in alternate
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orders, unless specifically stated to the contrary. In other words, any
sequence or order of steps
that may be described in this patent application does not, in and of itself,
indicate a requirement
that the steps be performed in that order. The steps of described processes
may be performed in
any order practical. Further, some steps may be performed simultaneously
despite being
described or implied as occurring non-simultaneously (e.g., because one step
is described after
the other step).
[0038] As used herein the following terms have the meaning given below:
[0039] "Aquatic animal" ¨ means, in a preferred embodiment, an animal, either
vertebrate or
invertebrate, which lives in the water for most or all of its lifetime.
[0040] "Targeted area" ¨ means, in a preferred embodiment, a specific body
area of an aquatic
animal where the measurement is needed.
[0041] "Microcontroller" ¨ means, in a preferred embodiment, a compact
integrated circuit that
incorporates a central processing unit, a memory, and input/output functions.
[0042] According to the present invention, the measurement of vital signs is
critical for the
treatment and management of many medical conditions for a variety of aquatic
animals. A smart
health monitoring system and method, which is capable of accurately as well as
frequently
measuring the vital signs including, but not limited to, body temperature,
blood pressure,
peripheral capillary oxygen saturation (Sp02), pulse rate, and respiration
rate is described in the
following detailed description of the present invention.
[0043] Reference will now be made in detail to a presently preferred
embodiment of the
invention, an example of which is illustrated in the accompanying drawings.
[0044] According to an exemplary embodiment of the present invention, a block
diagram of an
IoT-based smart health monitoring system is shown in FIG. 1. Referring now to
FIG. 1, the IoT-
based smart health monitoring system comprising: an attachable device 100
configured to be
attached on a targeted area of the aquatic animal, to collect various health
data of the aquatic
animal, and an external processing device 200 configured for machine learning
based processing
to infer the vital health signals in real time by implementing a machine
learning model.
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[0045] According to another exemplary embodiment of the present invention,
various functional
components of the attachable device 100 are shown in FIG. 2. Referring to FIG.
2, the
attachable device 100 comprising: an imaging device 101 configured to scan the
targeted area in
order to reconstruct an accurate 4-dimensional representation of a plurality
of micro blood
vessels present in the targeted area; a photoplethysmography (PPG) device 102
configured to
generate a PPG data from a plurality of sensors i.e. a temperature sensor 103,
a blood pressure
sensor 104, an oxygen saturation (Sp02) sensor 105, a pulse sensor 106, and a
respiration sensor
107; a microcontroller 108 with SPI and I2C, programmed to collect, process,
and classify the
data received from the imaging device 101 and the PPG device 102; a display
screen 109
configured to live display a processed data received from the microcontroller
108; a wireless
module 110 configured to transmit the processed data wirelessly to the
external processing
device 200 for machine learning based data processing; and a power module 111
configured to
supply power to the microcontroller 108.
[0046] According to another exemplary embodiment of the present invention, a
block diagram of
various internal components of the power module 111 is shown in FIG. 3.
Referring to FIG. 3,
the power module 111 comprising: a charging port 112, a battery 113, and a
power management
circuit 114, wherein the battery 113 is connected to the power management
circuit 114 which
feeds power to the microcontroller 108, and the battery 113 is charged via the
charging port 112
which receives the power when connected to an external charging station (not
shown).
[0047] According to the abovementioned embodiments of the present invention,
the attachable
device 100 of the smart health monitoring system is attached on the targeted
area of the aquatic
animal, wherein the imaging device 101 initially scans the targeted area in
order to reconstruct
the accurate 4-dimensional representation of the plurality of micro blood
vessels present in the
targeted area, and the PPG device 102 generates an initial PPG data by the
plurality of sensors
103, 104, 105, 106, 107 which detect various body parameters and relay the
detected data to the
PPG device 102. Further, the initial scan data and the initial PPG data are
fed to the
microcontroller 108 for automated data collecting, data processing and data
analyzing. The
processed scan data and PPG data is then live displayed on the display screen
109, which is also
transmitted to and stored on the external processing device 200 via the
wireless module 110, for
machine learning based processing. The processed scan data and PPG data stored
on the external
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processing device 200 hereinafter referred as "recorded data". Further, the
PPG device 102 keeps
generating the PPG data in real-time, which is then displayed on the display
screen 109 in real-
time after processed by the microcontroller 108. The processed PPG data
hereinafter referred as
"live PPG data". In the external processing device 200 using a data
visualization platform, the
scan data from the recorded data is used to reconstruct the detailed 4-
dimensional micro blood
vessels image, which is further fused with the PPG data from the recorded
data, in order to
generate a specific baseline profile of the vital health signal data for each
aquatic animal under
measurement or a specific species of the aquatic animal.
[0048] Moreover, the smart health monitoring system implements the machine
learning model
on the recorded data to generate a medical model of the individual aquatic
animal to obtain more
accurate vital health signals. For instance, a quantized machine learning
model runs on the
recorded data to process the recorded data in real time to infer various
measurements including,
but not limited to pulse rate, blood pressure, Sp02, and respiration rate. The
smart health
monitoring system utilizes a Deep Neural Network (DNN) model to generate a
correlation
function that converts the recorded data in real time to identify key features
including, but not
limited to systolic blood pressure (SBP), diastolic blood pressure (DBP),
Sp02, and respiration
rate. The smart health monitoring system further utilizes a Recurrent Neural
Network (RNN)
model to extract more detailed vital signs from the 4D micro blood vessel time-
series data
captured with the imaging device 101 to reconstruct aquatic animal's
hemodynamics system.
[0049] According to an embodiment of the present invention, the smart health
monitoring
system also tracks fluctuations in the vital health signal data through the
PPG device 102. In
addition, the present invention utilizes the machine learning models to infer
if variations from the
baseline health signal data are significant in both, aquatic animal under
measurement and
population.
[0050] According to an embodiment of the present invention, the external
processing device 200
can be selected from, but not limited to a personal computer, a laptop, a
tablet, a smartphone, a
mobile phone, and a personal digital assistance. Furthermore, the wireless
module 111 transmits
the processed scan data and PPG data via a wireless network selected from, but
not limited to
Wi-Fi, BluetoothTM, ZigBeeTM, Cellular, and Satellite.
Date Recue/Date Received 2021-09-14
[0051] According to another exemplary embodiment of the present invention, the
smart health
monitoring method is described in the form of a plurality of sequential steps
represented by a
flow diagram of the present invention as shown in FIG. 4. Referring to FIG. 4,
at step 1 in
starting with the process, the initial scan data and the initial PPG data is
recorded concurrently
from the imagine device 101 and the PPG device 102, respectively, and stored
on the external
processing device 200, wherein the recording of the data lasts for at least 5
minutes.
[0052] At step 2, the live PPG data is tracked from the PPG device 102 that is
still attached to
the aquatic animal.
[0053] At step 3, the recorded data is analyzed to determine temporal
correlations between the
recorded data received from the imagine device 101 and the PPG device 102, and
the live PPG
data generated by the PPG device 102; wherein the temporal correlations
correlate the recorded
data to the live PPG data by running the Deep Neural Network (DNN) model to
identify key
features including, but not limited to systolic blood pressure (SBP),
diastolic blood pressure
(DBP), Sp02, and respiration rate.
[0054] Further at step 4, the smart health monitoring system utilizes a
Recurrent Neural Network
(RNN) model to extract more detailed vital signs from the recorded data to
reconstruct aquatic
animal's hemodynamics system.
[0055] At step 5, a historical record of the medical data of the aquatic
animal is stored on a
memory device of the attachable device 100.
[0056] At step 6, the historical record of the medical data of the aquatic
animal is transmitted
wirelessly to the external processing device 200.
[0057] At step 7, the external processing device 200 receives one or more
medical threshold
values from the PPG device 102 and/or the plurality of sensors 103, 104, 105,
106, 107.
[0058] Finally at step 8, an alert is being transmitted to the external
processing device 200 in
response to the data in the historical records exceeding one or more of the
medical threshold
values.
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[0059] According to an embodiment of the present invention, the smart health
monitoring
method obtains the vital health signal prediction by implementing the machine
learning model,
wherein a plurality of sequential steps carried out by the machine learning
model using a
machine learning algorithm are described in a flow diagram of the present
invention as shown in
FIG. 5. Referring to FIG. 5, at step 1 in starting with the process, the
recorded data including the
initial scan data and the initial PPG data is collected to predict the vital
health signs.
[0060] At step 2, the collected data is further explored to fix all the data
inconsistencies by data
cleaning before training the machine learning models on it.
[0061] At step 3, the machine learning models are trained on the collected
data in order to make
prediction with respect to the vital health signs. The machine learning models
comprising the
Deep Neural Network (DNN) model and the Recurrent Neural Network (RNN) model.
[0062] At step 4, after training the machine learning models, the machine
learning models are
deployed in a real world system to predict the vital health signs. For
instance, the smart health
monitoring system of the present invention. After deploying the machine
learning models to the
smart health monitoring system, the initial scan data and the initial PPG data
generated by the
imaging device 101 and the PPG device 102, respectively can be input to the
machine learning
models, and the machine learning models can analyze the PPG data to determine
the vital health
signs prediction.
[0063] It is noted that various individual features of the inventive processes
and systems may be
described only in one exemplary embodiment herein. The particular choice for
description herein
with regard to a single exemplary embodiment is not to be taken as a
limitation that the particular
feature is only applicable to the embodiment in which it is described. All
features described
herein are equally applicable to, additive, or interchangeable with any or all
of the other
exemplary embodiments described herein and in any combination or grouping or
arrangement. In
particular, use of a single reference numeral herein to illustrate, define, or
describe a particular
feature does not mean that the feature cannot be associated or equated to
another feature in
another drawing figure or description. Further, where two or more reference
numerals are used in
the figures or in the drawings, this should not be construed as being limited
to only those
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embodiments or features, they are equally applicable to similar features or
not a reference
numeral is used or another reference numeral is omitted.
[0064] Although the subject matter has been described in language specific to
structural features
and/or operations, it is to be understood that the subject matter defined in
the appended claims is
not necessarily limited to the specific features and operations described
above. Rather, the
specific features and acts described above are disclosed as example forms of
implementing the
claims. Numerous modifications and alternative arrangements may be devised
without departing
from the spirit and scope of the described technology.
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